Hydrogen is a carbon-neutral fuel, so in theory it holds enormous potential. The use of hydrogen as a fuel for traditional internal combustion engines is becoming increasingly prominent. The authors now have the opportunity to retrofit a single-cylinder diesel research engine to an engine with hydrogen operation. For this reason, before that conversion, they prepared a comprehensive review study regarding hydrogen. Firstly, the study analyzes the most essential properties of hydrogen in terms of mixture formation and combustion compared to diesel. After that, it deals with indirect and direct injection, and what kind of combustion processes can occur. Since there is a possibility of pre-ignition, backfire, and knocking, the process can be dangerous in the case of indirect mixture formation, and so a short subsection is devoted to these uncontrolled combustion phenomena. The next subsection shows how important, in many ways, a special spark plug and ignition system are for hydrogen operation. The next part of the study provides a detailed presentation of the possible combustion chamber design for operation with hydrogen fuel. The last section reveals how many parameters can be focused on analyzing the hydrogen’s combustion process. The authors conclude that intake manifold injection and a Heron-like combustion chamber design, with a special spark plug with an ignition system, would be an appropriate solution.

Achieving sustainable mobility is a crucial factor in maintaining long-term economic growth without adverse effects on human health and the environment. E-fuels, such as the promising oxymethylene ether (OME), can contribute to sustainable road transport. However, this compound does not meet the requirements of EN590; thus, it is unsuitable for unmodified diesel engines. This work aims to improve the applicability of OME by blending it with n-decanol, which can also be produced sustainably. Combustion and emissions were investigated in a medium-duty commercial diesel engine with different binary and ternary mixtures of oxymethylene ether, n-decanol, and B7 diesel. Laboratory analysis of six key mixture parameters revealed that the formulated blends met the EN590 requirements, with the exception of that of density. The results demonstrated that the created mixtures, including one without any diesel fuel, can be efficiently utilized in unmodified diesel engines. OME’s beneficial effects on combustion and emission were preserved while its viability was improved; a maximum increase of 7.6% in brake thermal efficiency was observed, alongside a potential decrease of nearly 70% in PM emissions at unaltered NO_x levels.

Artificial intelligence (AI) methods have rapidly become a best practice in various industrial applications due to their exceptional predictive abilities. Compared to traditional physical and chemical models, AI-driven approaches typically offer faster computation times without sacrificing accuracy. This makes them well-suited for enhancing or replacing conventional methods in engine technology. Building on this potential, our previous work focused on developing AI-based tools to accelerate the development of novel e-fuels for internal combustion engines. These AI tools require representative training datasets created through expensive engine dyno measurements. To address this challenge, we developed a general methodology to improve the cost-efficiency of dataset creation. This paper presents the experimental validation of this methodology by assessing its performance on seven different predictive tasks. Following the proposed framework, we designed and conducted an engine dyno experiment, then developed AI models to predict seven critical engine performance and emissions parameters: center of heat release, ignition delay, peak combustion temperature, peak pressure rise rate, brake thermal efficiency, exhaust opacity and NOx emissions. The results demonstrated the effectiveness of the methodology, with five out of seven models achieving excellent predictive performance on unseen test data (R2 > 0.97). Peak pressure rise rate and opacity models had slightly lower performance (R2 > 0.94), however, given the well-known challenges associated with predicting these parameters, the results are acceptable.

Accurate physical property prediction of newly developed compounds is vital across various industrial sectors, particularly for the customization of fuels and additives. Artificial intelligence (AI) has recently emerged as a best practice in numerous industrial fields because of its capacity for swift and precise calculations. While conventional methods such as group contribution models have been used to estimate physical properties from molecular structure, AI offers significant potential for improving the predictive accuracy. Thus, this work focuses on developing an AI model to predict key properties – boiling points, melting points, and flashpoints – of various hydrocarbons, to demonstrate the AI's superior predictive capabilities. A dataset consisting of 202 organic compounds was created and multilayer perceptron (MLP) neural networks were employed to estimate these properties using atomic numbers, functional groups, and molecular complexity as inputs. The model's performance was evaluated and compared against conventional group contribution methods on the same dataset. The AI model was further tested on new acetal compounds, revealing its broader applicability in both fuel and chemical sectors. Results show that the AI outperformed conventional methods, excelling in 5 out of 8 hydrocarbon types for boiling points, 7 for melting points, and all 8 for flashpoints.

Heavy-duty diesel engines are an essential part of road transportation. Since viable alternatives are not expected in the short and medium term, the problematic emission characteristics of compression ignition engines have to be addressed. Low temperature combustion (LTC) is an alternative combustion method for compression ignition engines that allows low particulate matter and nitrogen oxide emissions while improving efficiency. To overcome the difficulties of market introduction, the realization of such alternative combustion methods should come with marginal engine modifications. Thus, this work investigates the possible realization of LTC in an unmodified diesel engine. LTC methods with and without injection strategy modifications were also studied to provide sufficient recommendations for other researchers. It was concluded that techniques requiring early direct injection, such as homogeneous charge compression ignition (HCCI) require a narrow cone angle injector to reduce wall impingement. It was also concluded that modulated kinetics (MK) type LTC can be easily achieved by applying a conventional injection strategy and high amounts of cooled exhaust gas recirculation. The realized MK combustion resulted in an enhanced NOx-PM trade-off and a lower peak pressure rise rate compared to normal operation.